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Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification

Iran Journal of Computer Science 2024年第1期8卷 185-215页

作者： Kia, Marjan Sadeghi, Soroush Safarpour, Homayoun Kamsari, Mohammadreza Jafarzadeh Ghoushchi, Saeid Ranjbarzadeh, Ramin Department of Business Information System Business Analysis (ISBA) Park University Parkville United States School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Software Engineering University of Szeged Szeged Hungary Faculty of Electrical Engineering Malek-Ashtar University of Technology(MUT) Esfahan Iran Faculty of Industrial Engineering Urmia University of Technology Urmia Iran School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland

This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an attention mechanism. The primary objective was to accurately detect the presence or absence of brain tumors—a critical challenge in medical diagnosis. The methodology was validated on a comprehensive dataset comprising 3264 brain MRI scans, systematically divided into training, validation, and testing subsets. Preprocessing techniques tailored for MRI data were employed to enhance input quality. Each model was initially developed and evaluated individually to establish a performance baseline before their integration. The proposed ensemble technique capitalized on the complementary strengths of these models to enhance classification accuracy and robustness. The inclusion of an attention mechanism further refined the models by emphasizing critical regions in the MRI scans, thereby boosting predictive performance. In addition, data augmentation techniques were applied to mitigate overfitting and increase dataset resilience. Performance metrics, including precision, accuracy, and recall, demonstrated significant improvements across all evaluations, particularly for the models incorporating the attention mechanism. These findings highlight the potential of combining advanced DL architectures with attention mechanisms as a promising strategy for precise and reliable brain tumor classification in MRI scans. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AlexNet Attention mechanism Brain tumor classification MobileNet ResNet50

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing

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Computer Systems Science & engineering 2023年第11期47卷 2259-2274页

作者： Kaili Shao Hui Fu Ying Song Bo Wang Faculty of Engineering Huanghe Science and Technology UniversityZhengzhou 450003China Computer School Beijing Information Science and Technology UniversityBeijing100192China Software Engineering College Zhengzhou University of Light IndustryZhengzhou450001China

To serve various tasks requested by various end devices with different requirements,end-edge-cloud(E2C)has attracted more and more attention from specialists in both academia and industry,by combining both benefits of edge and cloud *** nowadays,E2C still suffers from low service quality and resource efficiency,due to the geographical distribution of edge resources and the high dynamic of network topology and user *** address these issues,this paper focuses on task offloading,which makes decisions that which resources are allocated to tasks for their *** paper first formulates the problem into binary non-linear programming and then proposes a particle swarm optimization(PSO)-based algorithm to solve the *** proposed algorithm exploits an imbalance mutation operator and a task rescheduling approach to improve the performance of *** proposed algorithm concerns the resource heterogeneity by correlating the probability that a computing node is decided to process a task with its capacity,by the imbalance *** task rescheduling approach improves the acceptance ratio for a task offloading solution,by reassigning rejected tasks to computing nodes with available *** simulated experiments are *** the results show that the proposed offloading algorithm has an 8.93%–37.0%higher acceptance ratio than ten of the classical and up-to-date algorithms,and verify the effectiveness of the imbalanced mutation and the task rescheduling.

关键词： Cloud computing edge computing edge cloud task scheduling task offloading particle swarm optimization

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Robust Classification of Similar Yoga Poses based on Grouping and Hierarchical Inference 6

Robust Classification of Similar Yoga Poses based on Groupin...

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6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Huynh, Huu-Phuc Nguyen, Duc-Hung Nguyen, Thanh Thao-Ly Nguyen, Mai-Vinh Nguyen, Quang-Thuc University of Science VNU-HCM Vietnam National University Faculty of Information Technology and Software Engineering Laboratory Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350327410

The COVID-19 pandemic has had a negative impact on people's health, leading to an increased demand for self-improvement methods, such as Yoga. However, practicing Yoga without guidance is challenging due to the complexity of poses and the risk of injury. The authors propose a new method for classifying Yoga poses from images to address this issue. Although previous studies have achieved up to 99.33% accuracy, they involve only 3 classes, only a fraction of the 82 poses in the Yoga-82 dataset used in this study. We enhance a pre-trained DenseNet121 and achieve an accuracy of 88.89%, higher than the method by Verma ***. at CVPR workshop 2020, which reached 79.31%. Although data for training is just a subset of Yoga82 dataset, the model has less complexity, about 10 million parameters. Moreover, using a customized DenseNet121 model as a baseline, the authors enhanced its accuracy by incorporating a hierarchical structure and grouping data. Then, the authors proposed two methods, XGBoost and YOLOv5, working on parent labels to improve the accuracy of similar-looking poses and get a total accuracy of 87.33%. The approaches demonstrated in this paper could be applied to other sports or physical actions, indicating their potential for broader use. © 2023 IEEE.

关键词： COVID-19

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Self-adaptive Bat Algorithm With Genetic Operations

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1284-1294页

作者： Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou H.Vincent Poor School of Software Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electrical Engineering Princeton UniversityPrinceton NJ 08544 USA IEEE

Swarm intelligence in a bat algorithm(BA)provides social *** operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization *** integration provides an opportunity for improved search ***,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions *** from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated ***,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and *** by these exemplars,SBAGO improves both BA’s efficiency and global search *** evaluate this approach by using 29 widely-adopted problems from four test *** is also evaluated by a real-life optimization problem in mobile edge computing *** results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.

关键词： Bat algorithm(BA) genetic algorithm(GA) hybrid algorithm learning mechanism meta-heuristic optimization algorithms

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Observation points classifier ensemble for high-dimensional imbalanced classification

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CAAI Transactions on Intelligence technology 2023年第2期8卷 500-517页

作者： Yulin He Xu Li Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen UniversityShenzhenChina Faculty of Information Technology Macao University of Science and TechnologyMacaoChina School of Computing National University of SingaporeSingaporeSingapore

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction ***,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as ***,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data ***,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown *** experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data *** results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC *** demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

关键词： classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism

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Comprehensive System for Facilitating Professional Growth by Linking Mentees and Mentors in information technology 15

Comprehensive System for Facilitating Professional Growth by...

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15th International Conference on Knowledge and Systems engineering, KSE 2023

作者： Nguyen, Truc Ton, Quynh Tran, Cuong Dinh, Dung Le, Trong University of Information Technology Faculty of Software Engineering Ho Chi Minh Viet Nam Vietnam National University Ho Chi Minh Viet Nam

ISBN: (纸本)9798350329742

This paper presents an innovative system designed to enhance connections between individuals seeking mentorship (mentees) and experienced professionals (mentors) in the field of information technology (IT). The system consists of two separate websites: one for mentees and mentors to connect and collaborate, and another for administrators to manage user accounts and handle payment transactions. The technical core of this system focuses on a robust face recognition mechanism for mentor verification. This mechanism involves processes such as facial detection, vector representation, and face comparison using the Euclidean distance metric. Its primary purpose is to verify the authenticity and expertise of mentors, ensuring that only genuine and high-quality mentors are retained in the system. Furthermore, the system introduces an innovative feature that enables the creation of collaborative workspaces for both mentors and mentees. These workspaces offer a flexible environment for interactive activities, including real-time chat, video calls, recording capabilities, and seamless file sharing. To demonstrate the system's effectiveness, we conducted comprehensive experimental evaluations. The results conclusively show that the system excels at facilitating successful connections between mentees and qualified mentors in the field of IT. © 2023 IEEE.

关键词： Face recognition

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ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analytics of Attack Patterns

ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analyt...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Fiza, Inbasat Khurshid, Fizza Fatima, Manahil Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan College of Engineering Seoul National University Technology Management Economics and Policy Program 1 Gwanak-Ro Gwanak-Gu Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The ATT&CK MITRE framework serves as an expansive repository of adversary tactics, techniques, and procedures. Given the sheer volume of these intricate attack patterns, the conventional manual navigation method proves to be time-intensive and less efficient for security analysts. As a remedy, there arises a compelling necessity to present the rich content encapsulated within the ATT&CK MITRE repository through interactive visualizations. In this paper ATT&CKViz an interactive web-based cyber-Attack patterns visualization tool is presented that aims to empower security analysts with the ability to effortlessly explore the repository from diverse angles, which facilitates deep insights into cyber threats. The aim is to understand cyber threat efficiently with a timely response. The empirical evaluations depict that effectiveness and user-friendliness of ATT&CKViz. © 2024 IEEE.

关键词： Cybersecurity

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A Goal-Oriented Approach to Hybrid Learning in Higher Education Through the Use of the Ecosystem Model: A Case Study 7th

A Goal-Oriented Approach to Hybrid Learning in Higher Educat...

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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Lauthan, Shameera Saurty, Krishnah Moortee Department of Software Engineering Faculty of Information and Communication Technology Université des Mascareignes SD Campus Beau Plan Mauritius Department of Management Faculty of Business and Management Université des Mascareignes SD Campus Beau Plan Mauritius

ISBN: (纸本)9789819950140

Higher education in Mauritius is undergoing major curricular changes. To cope with these changes and to reach a wider and more diversified public, Higher Education Institutions (HEIs) are using information and Communication technology (ICT) tools like customized teaching, online learning and virtual campus. Online learning platforms widely adopted by Université des Mascareignes (UdM) offer learners and teachers hybrid education—blended learning, referred to as technology-mediated instruction. Hybrid learning mixes online educational resources and online engagement with traditional classroom techniques. When hybridizing courses, the pedagogical and technical teaching methods are reconsidered. This article examines the application of the Hy-Sup Model, a hybrid system with an Ecosystem typology capable of supporting reusable lifelong learning and facilitating decision-making by concerned parties at different levels. The Hy-Sup model based on a goal-oriented approach has the objective to provide with the Ecosystem typology a method to design, implement, analyze, evaluate and improve such systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ecosystems

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LaneDraw: Cascaded lane and its bifurcation detection with nested fusion

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Science China(Technological Sciences) 2021年第6期64卷 1238-1249页

作者： REN KeYan HOU HaoChen LI SiYang YUE TianYi Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Engineering Research Center for IoT Software and Systems Beijing 100124China

Lane and its bifurcation detection is a vital and active research topic in low cost camera-based autonomous driving and advanced driver assistance system(ADAS). The common lane detection pipeline usually predicts lane segmentation mask firstly, and then makes line fitting by parabola or spline post-processing. However, if the speed of the lane and its bifurcation detection is fast and robust enough, we think curve fitting is not a necessary step. The goal of this work is to get accurate lane segmentation,identification of every lane, adaptability of lane numbers and the right combination of lane bifurcation. In this work, we relabeled lane and its bifurcation with solid line if the image of Tu Simple dataset has both of them. In the data training process, we apply a data balance strategy for the heavily biased lane and non-lane data. In such a way, we develop a competitive cascaded instance lane detection model and propose a novel bifurcation pixel embedding nested fusion method based on full binary segmentation pixel embedding with self-grouping cluster, called Lane Draw. Our method discards curve fitting process, therefore it reduces the complexity of post-processing and increases detection speed at 35 fps. Moreover, the proposed method yields better performance and high accuracy on the relabeled Tu Simple dataset. To the best of our knowledge, this is the first attempt in 2 D lane and bifurcation detection, which more often happens in actual situations.

关键词： lane detection bifurcation fusion instance segmentation autonomous driving

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